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In this work, we present a novel method to tackle the token generationchallenge in Vision Language Models (VLMs) for video and image understanding,called LLaMA-VID. Current VLMs, while proficient in tasks like image captioningand visual question answering, face computational burdens when processing longvideos due to the excessive visual tokens. LLaMA-VID addresses this issue byrepresenting each frame with two distinct tokens, namely context token andcontent token. The context token encodes the overall image context based onuser input, whereas the content token encapsulates visual cues in each frame.This dual-token strategy significantly reduces the overload of long videoswhile preserving critical information. Generally, LLaMA-VID empowers existingframeworks to support hour-long videos and pushes their upper limit with anextra context token. It is proved to surpass previous methods on most of video-or image-based benchmarks. Code is availablehttps://github.com/dvlab-research/LLaMA-VID}{https://github.com/dvlab-research/LLaMA-VID

LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models